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The purpose of this study is to apply inverse dynamics control for a six degree of freedom flight simulator motion system. Imperfect compensation of the inverse dynamic control is intentionally introduced in order to simplify the implementation of this approach. The control strategy is applied in the outer loop of the inverse dynamic control to counteract the effects of imperfect compensation. The control strategy is designed using H ∞ theory. Forward and inverse kinematics and full dynamic model of a six degrees of freedom motion base driven by electromechanical actuators are briefly presented. Describing function, acceleration step response and some maneuvers computed from the washout filter were used to evaluate the performance of the controllers.

System identification consists of the development of techniques for model estimation from experimental data, demanding no previous knowledge of the process. Aeroelastic models are directly influence of the benefits of identification techniques, basically because of the difficulties related to the modelling of the coupled aero- and structural dynamics. In this work a comparative study of the bilinear dynamic identification of a helicopter blade aeroelastic response is carried out using artificial neural networks is presented. Two neural networks architectures are considered in this study. Both are variations of static networks prepared to accomodate the system dynamics. A time delay neural networks (TDNN) for response prediction and a typical recurrent neural networks (RNN) are used for the identification. The neural networks have been trained by Levemberg-Marquardt algorithm. To compare the performance of the neural networks models, generalization tests are produced where the aeroelastic responses of the blade in flapping and torsion motions at its tip due to noisy pitching angle are presented. An analysis in frequency of the signals from simulated and the emulated models are presented. In order to perform a qualitative analysis, return maps with the simulation results generated by the neural networks are presented

Flutter is a dynamic aeroelastic instability that involves the interaction of aerodynamic, elastic, and inertial forces. This instability may occur in aircraft surfaces, like wings and tails, leading them to a divergent oscillatory motion. A classical two-degrees-of-freedom flutter is an interaction of bending and torsional modes of vibration of a structure. A flexible mount system has been developed for flutter tests with rigid wings in wind tunnels. This flexible mount has to provide a well-defined two-degrees-of-freedom system on which rigid wings encounter flutter. Active control schemes for flutter suppression can be tested using this experimental set-up. An aeroelastic model is formulated to simulate the aeroelastic behaviour of this system. The equations of motion are developed using Lagrange's equations and the Principle of Virtual Work, resulting in a state space representation. This is a convenient way to determine pitch and plunge time responses for several initial conditions and velocities. Using this model, a state feedback controller of the form u ¼ 2Kx is determined. The wind tunnel model is a rectangular wing with a NACA 0012 airfoil section and a trailing edge control surface as actuator. The main goal is to suppress flutter and to maintain the stability of the closed loop system.

This paper presents a closed-form solution for the direct dynamic model of a flight simulator motion base. The motion base consists of a six degree-of-freedom (6DOF) Stewart platform robotic manipulator driven by electromechanical actuators. The dynamic model is derived using the Newton–Euler method. Our derivation is closed to that of Dasgupta and Mruthyunjaya (1998, “Closed Form Dynamic Equations of the General Stewart Platform Through the Newton–Euler Approach,” Mech. Mach. Theory, 33(7), pp. 993–1012), however, we give some insights into the structure and properties of those equations, i.e., a kinematic model of the universal joint, inclusion of electromechanical actuator dynamics and the full dynamic equations in matrix form in terms of Euler angles and platform position vector. These expressions are interesting for control, simulation, and design of flight simulators motion bases. Development of a inverse dynamic control law by using coefficients matrices of dynamic equation and real aircraft trajectories are implemented and simulation results are also presented.